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Received β€” 26 March 2026 ⏭

Unannotated noncoding transcripts as a source of intratumor heterogeneity in malignant cell states

βœ‡(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Xiaolan Zhou Β· Limin Lin Β· Yu Chen Β· Guangrui Yang Β· Ting Ni Β· Zhaozhao Zhao
23 March 2026 at 18:00

Sci China Life Sci. 2026 Mar 16. doi: 10.1007/s11427-025-3273-6. Online ahead of print.

ABSTRACT

Phenotypic diversity of malignant cells within a tumor underlies intratumor heterogeneity (ITH), a key determinant of cancer metastasis and treatment failure. However, the molecular mechanisms driving this heterogeneity are poorly understood. Here, we curated and analyzed a cohort of 3' tag-based single-cell RNA-seq covering 12 common cancer types. We identified thousands of poly(A) site (PAS) peaks representing the 3' ends of previously unannotated transcripts, whose expression is widely associated with diverse malignant cellular states. By integrating multi-omics data, we characterized the expression patterns and epigenetic landscape of these unannotated PAS peak-associated transcripts (UPTs). The expression heterogeneity of UPTs was supported by multi-region sampling bulk RNA-seq data and recapitulated within cancer cell lines. As proof of principle validation, functional experiments confirmed that two noncoding UPTs promoted the proliferation and migration of lung cancer cells. Our results suggest that epigenetic activation of unannotated noncoding transcripts might represent a previously unrecognized mechanism contributing to transcriptomic ITH.

PMID:41870780 | DOI:10.1007/s11427-025-3273-6

Unannotated noncoding transcripts as a source of intratumor heterogeneity in malignant cell states

βœ‡Omics In Lung
By: Xiaolan Zhou Β· Limin Lin Β· Yu Chen Β· Guangrui Yang Β· Ting Ni Β· Zhaozhao Zhao
23 March 2026 at 18:00

Sci China Life Sci. 2026 Mar 16. doi: 10.1007/s11427-025-3273-6. Online ahead of print.

ABSTRACT

Phenotypic diversity of malignant cells within a tumor underlies intratumor heterogeneity (ITH), a key determinant of cancer metastasis and treatment failure. However, the molecular mechanisms driving this heterogeneity are poorly understood. Here, we curated and analyzed a cohort of 3' tag-based single-cell RNA-seq covering 12 common cancer types. We identified thousands of poly(A) site (PAS) peaks representing the 3' ends of previously unannotated transcripts, whose expression is widely associated with diverse malignant cellular states. By integrating multi-omics data, we characterized the expression patterns and epigenetic landscape of these unannotated PAS peak-associated transcripts (UPTs). The expression heterogeneity of UPTs was supported by multi-region sampling bulk RNA-seq data and recapitulated within cancer cell lines. As proof of principle validation, functional experiments confirmed that two noncoding UPTs promoted the proliferation and migration of lung cancer cells. Our results suggest that epigenetic activation of unannotated noncoding transcripts might represent a previously unrecognized mechanism contributing to transcriptomic ITH.

PMID:41870780 | DOI:10.1007/s11427-025-3273-6

Received β€” 17 March 2026 ⏭

FAPE-IR: Frequency-Aware Planning and Execution Framework for All-in-One Image Restoration

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Jingren Liu Β· Shuning Xu Β· Qirui Yang Β· Yun Wang Β· Xiangyu Chen Β· Zhong Ji
16 March 2026 at 12:00
arXiv:2511.14099v3 Announce Type: replace-cross Abstract: All-in-One Image Restoration (AIO-IR) aims to develop a unified model that can handle multiple degradations under complex conditions. However, existing methods often rely on task-specific designs or latent routing strategies, making it hard to adapt to real-world scenarios with various degradations. We propose FAPE-IR, a Frequency-Aware Planning and Execution framework for image restoration. It uses a frozen Multimodal Large Language Model (MLLM) as a planner to analyze degraded images and generate concise, frequency-aware restoration plans. These plans guide a LoRA-based Mixture-of-Experts (LoRA-MoE) module within a diffusion-based executor, which dynamically selects high- or low-frequency experts, complemented by frequency features of the input image. To further improve restoration quality and reduce artifacts, we introduce adversarial training and a frequency regularization loss. By coupling semantic planning with frequency-based restoration, FAPE-IR offers a unified and interpretable solution for all-in-one image restoration. Extensive experiments show that FAPE-IR achieves state-of-the-art performance across seven restoration tasks and exhibits strong zero-shot generalization under mixed degradations.
Received β€” 25 February 2026 ⏭

BEAT: Visual Backdoor Attacks on VLM-based Embodied Agents via Contrastive Trigger Learning

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Qiusi Zhan Β· Hyeonjeong Ha Β· Rui Yang Β· Sirui Xu Β· Hanyang Chen Β· Liang-Yan Gui Β· Yu-Xiong Wang Β· Huan Zhang Β· Heng Ji Β· Daniel Kang
24 February 2026 at 13:00
arXiv:2510.27623v3 Announce Type: replace Abstract: Recent advances in Vision-Language Models (VLMs) have propelled embodied agents by enabling direct perception, reasoning, and planning task-oriented actions from visual inputs. However, such vision-driven embodied agents open a new attack surface: visual backdoor attacks, where the agent behaves normally until a visual trigger appears in the scene, then persistently executes an attacker-specified multi-step policy. We introduce BEAT, the first framework to inject such visual backdoors into VLM-based embodied agents using objects in the environments as triggers. Unlike textual triggers, object triggers exhibit wide variation across viewpoints and lighting, making them difficult to implant reliably. BEAT addresses this challenge by (1) constructing a training set that spans diverse scenes, tasks, and trigger placements to expose agents to trigger variability, and (2) introducing a two-stage training scheme that first applies supervised fine-tuning (SFT) and then our novel Contrastive Trigger Learning (CTL). CTL formulates trigger discrimination as preference learning between trigger-present and trigger-free inputs, explicitly sharpening the decision boundaries to ensure precise backdoor activation. Across various embodied agent benchmarks and VLMs, BEAT achieves attack success rates up to 80%, while maintaining strong benign task performance, and generalizes reliably to out-of-distribution trigger placements. Notably, compared to naive SFT, CTL boosts backdoor activation accuracy up to 39% under limited backdoor data. These findings expose a critical yet unexplored security risk in VLM-based embodied agents, underscoring the need for robust defenses before real-world deployment.
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